Dynamic life reactor maintenance platform and method

Abstract

The invention provides a dynamic life reactor maintenance platform and method, and relates to the technical field of power management, and the platform comprises a data collection module which is used for obtaining the operation state data of a target reactor, and the operation state data comprises an operation condition data column and an operation environment data column; the state analysis module is used for extracting scene operation state distribution of the target reactor and defining a scene life consumption coefficient; the dynamic life prediction module is used for initializing a dynamic life model to perform dynamic life prediction; the maintenance strategy generation module is used for updating the maintenance strategy of the target reactor according to the dynamic life prediction result; and the maintenance report generation module is used for applying the updated maintenance strategy of the target reactor and generating a maintenance report of the target reactor. According to the method, the technical problem that the formulation of a maintenance strategy is lack of pertinence and flexibility due to neglect of the actual operation environment and load condition change of the electric reactor because most of traditional electric reactors are based on regular maintenance is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a dynamic life reactor maintenance platform and method. Background Art

[0002] Reactors are important regulating devices in power systems and are widely used to control current, voltage and reactive power. The normal operation of reactors is crucial to the stability and reliability of power systems. However, most traditional reactors are based on periodic maintenance, that is, inspection and maintenance at fixed time intervals. Although this method has a certain regularity, it ignores the actual operating environment and load condition changes of the reactor, resulting in excessive maintenance, that is, when the reactor is actually in good condition, it is still maintained at a fixed cycle, which increases unnecessary costs and wastes resources, or insufficient maintenance, that is, when the reactor is in a harsh environment or high load, the risk of failure increases, but the fixed cycle maintenance cannot respond in time, which may cause failures. Summary of the invention

[0003] This application provides a dynamic life reactor maintenance platform and method, aiming to solve the technical problem that most traditional reactors are based on periodic maintenance, ignoring the actual operating environment and load condition changes of the reactor, resulting in the lack of specificity and flexibility in the formulation of maintenance strategies.

[0004] The first aspect disclosed in the present application provides a dynamic life reactor maintenance platform, the platform comprising: a data acquisition module, the data acquisition module is used to obtain operating status data of the target reactor, wherein the operating status data includes an operating condition data column and an operating environment data column; a state analysis module, the state analysis module is used to extract the scenario operating status distribution of the target reactor according to the operating status data, and define a scenario life consumption coefficient according to the scenario operating status distribution; a dynamic life prediction module, the dynamic life prediction module is used to initialize a dynamic life model for dynamic life prediction according to the scenario life consumption coefficient, wherein the dynamic life model is a life prediction model under a standard operating state; a maintenance strategy generation module, the maintenance strategy generation module is used to update the target reactor maintenance strategy according to the dynamic life prediction result; a maintenance report generation module, the maintenance report generation module is used to apply the updated target reactor maintenance strategy and generate a maintenance report for the target reactor.

[0005] The second aspect disclosed in the present application provides a dynamic life reactor maintenance method, which is implemented through the above-mentioned dynamic life reactor maintenance platform, and the method includes: obtaining operating status data of the target reactor, wherein the operating status data includes an operating condition data column and an operating environment data column; extracting the scenario operating status distribution of the target reactor based on the operating status data, and defining a scenario life consumption coefficient based on the scenario operating status distribution; initializing a dynamic life model for dynamic life prediction based on the scenario life consumption coefficient, wherein the dynamic life model is a life prediction model under a standard operating state; updating the target reactor maintenance strategy based on the dynamic life prediction result; applying the updated target reactor maintenance strategy, and generating a maintenance report for the target reactor.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: By acquiring the operating condition data and operating environment data of the reactor, the reactor status can be fully monitored. These data provide comprehensive basic data support for life prediction and maintenance strategies. According to the operating data of the reactor, the scenario operating status distribution is extracted, and the scenario life consumption coefficient is defined. It can accurately identify and analyze the life consumption law of the reactor in different environments and working conditions. Through the definition and calculation of the scenario life consumption coefficient, the relative life consumption rate of the reactor in a specific scenario is quantified, which makes the life prediction more accurate and provides a quantitative basis for optimizing the maintenance strategy. According to the scenario life consumption coefficient, the dynamic life model is initialized. The dynamic life model performs dynamic life prediction based on the standard operating state, which greatly improves the accuracy of the reactor life prediction. According to the dynamic life prediction results, the maintenance of the reactor is optimized. The strategy is updated in real time. The maintenance strategy is no longer fixed, but is dynamically adjusted according to the actual operating status, life consumption and risk assessment results to ensure the efficiency and accuracy of maintenance work. By predicting the life status of the reactor in advance, potential faults can be identified and prevented in advance, avoiding sudden failures of the reactor at critical moments. This proactive maintenance method significantly reduces the failure rate and unplanned downtime; the updated maintenance strategy is applied, and maintenance reports are automatically recorded and generated to provide managers with a clear reactor status and management basis. At the same time, automated report generation reduces the errors and uncertainties of manual records, greatly improving management efficiency. Through continuous updating and feedback, each maintenance operation and dynamic life prediction form a closed-loop management system, gradually optimizing the management strategy of the reactor and improving the management efficiency and effectiveness of the reactor.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the structure of a dynamic life reactor maintenance platform provided in an embodiment of the present application.

[0009] Figure 2 A schematic flow chart of a method for maintaining a reactor with a dynamic life span provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: data acquisition module 10 , state analysis module 20 , dynamic life prediction module 30 , maintenance strategy generation module 40 , maintenance report generation module 50 . DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a dynamic life reactor maintenance platform and method to solve the technical problem that most traditional reactors are based on periodic maintenance, ignoring the actual operating environment and load condition changes of the reactor, resulting in the lack of specificity and flexibility in the formulation of maintenance strategies.

[0012] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a dynamic life reactor maintenance platform, the platform comprising: A data acquisition module 10, the data acquisition module 10 is used to obtain the operating status data of the target inductor, wherein the operating status data includes an operating condition data column and an operating environment data column; a state analysis module 20, the state analysis module 20 is used to extract the scenario operating status distribution of the target inductor according to the operating status data, and define the scenario life consumption coefficient according to the scenario operating status distribution; a dynamic life prediction module 30, the dynamic life prediction module 30 is used to initialize the dynamic life model for dynamic life prediction according to the scenario life consumption coefficient, wherein the dynamic life model is a life prediction model under a standard operating state; a maintenance strategy generation module 40, the maintenance strategy generation module 40 is used to update the maintenance strategy of the target inductor according to the dynamic life prediction result; a maintenance report generation module 50, the maintenance report generation module 50 is used to apply the updated maintenance strategy of the target inductor and generate a maintenance report for the target inductor.

[0014] Furthermore, the operating status data includes operating condition data and operating environment data between the most recent maintenance completion and the current collection node.

[0015] Furthermore, before extracting the scenario operating state distribution of the target reactor according to the operating state data, the execution steps include: Based on big data, life consumption samples of the target inductor are obtained, and the life consumption samples include multiple sample operating states and multiple associated sample life performances; the standard operating state and standard life performance of the target inductor are obtained, and in combination with feature engineering, multiple state difference features between the multiple sample operating states and the standard operating states are obtained; in combination with feature engineering, multiple sample life consumption coefficients of the multiple sample life performances and the standard life performances are calculated, wherein the sample life consumption coefficient is the ratio of the sample life performance to the standard life performance; the multiple state difference features are associated with the multiple sample life consumption coefficients, and a numerically driven consumption coefficient model is constructed and trained.

[0016] Furthermore, according to the operating status data, the scenario operating status distribution of the target reactor is extracted, and the execution steps include: The operating status data is parsed to perform operating status clustering and generate a status clustering result, wherein the status clustering result includes a plurality of typical status classes; a plurality of typical scenario operating states of the plurality of typical status classes are determined based on density distribution; a plurality of typical scenario operating states are mapped to the consumption coefficient model to obtain a plurality of life consumption coefficients corresponding to the plurality of typical scenario operating states, wherein the life consumption coefficient is a relative life consumption rate under the typical scenario operating state.

[0017] Furthermore, according to the operating status data, the scenario operating status distribution of the target reactor is extracted, and the execution step further includes: Analyze the state clustering results, and count the number of sample points within the multiple typical state classes; calculate the sample proportions of the multiple typical scene operation states based on the multiple number of sample points within the classes, and output them as the scene operation state distribution.

[0018] Furthermore, the scenario life consumption coefficient is defined according to the scenario operation state distribution, and the execution steps include: Based on the scene operation state distribution, the weighted weights of the multiple life consumption coefficients are determined; the multiple life consumption coefficients corresponding to the multiple typical scene operation states are weighted to obtain a weighted life consumption coefficient, which is defined as the scene life consumption coefficient.

[0019] Furthermore, the execution steps of the platform also include: Calling the historical maintenance record of the target reactor, wherein the historical maintenance record includes historical operating condition data and fault marks; statistically analyzing the historical maintenance record to obtain the fault probability characteristics of the historical operating condition data, and establishing a mapping relationship between the operating condition data and the fault probability, and storing it as a scenario-based failure rate model of the target reactor; based on the scenario-based failure rate model, evaluating the real-time failure probability of the target reactor, and generating a burst maintenance instruction if the real-time failure probability does not meet the preset failure probability constraint.

[0020] Through the subsequent detailed description of a dynamic life reactor maintenance method in this specification, technical personnel in this field can clearly understand a dynamic life reactor maintenance platform in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0021] Embodiment 2, based on the same inventive concept as a dynamic life reactor maintenance platform in the above embodiment, such as Figure 2 As shown, an embodiment of the present application provides a dynamic life reactor maintenance method, the method comprising: The operating status data of the target reactor is acquired, wherein the operating status data includes an operating condition data column and an operating environment data column.

[0022] A variety of sensors are installed in the target reactor and its operating environment. The selection of these sensors needs to be able to collect relevant operating data and environmental parameters, including temperature sensors, humidity sensors, current sensors, voltage sensors, load sensors, etc. According to the structural characteristics of the reactor, the sensors are reasonably arranged to ensure that the collected data truly reflects the operating status of the reactor.

[0023] By installing a variety of sensors, set a suitable collection frequency for data collection. The collection frequency can be seconds, minutes or longer time intervals, which is determined by the working nature of the reactor. The collected data are integrated and arranged in chronological order to form time series data, and the operating condition data column and the operating environment data column are obtained. Among them, the operating condition data column reflects the actual operating status of the reactor, including current, voltage, load and other information. This type of data is directly related to the life consumption and maintenance requirements of the reactor; the operating environment data column reflects the external environment of the reactor, including temperature, humidity, etc. Changes in environmental parameters will affect the performance of the reactor and its life prediction.

[0024] According to the operating status data, a scenario operating status distribution of the target reactor is extracted, and a scenario life consumption coefficient is defined according to the scenario operating status distribution.

[0025] The collected operating status data is analyzed and clustered for the operating status of the reactor. Clustering methods include K-means, etc. The operating status data is divided into different status categories by calculating the similarity between features. Each clustering result represents a typical operating status, which are classified into different scenarios, such as low-load operation, high-temperature and high-load operation, or normal operation.

[0026] Based on the clustering results, the proportion of each typical state in the total data is calculated, that is, the density of sample points in each cluster. This density reflects the operating frequency of the reactor in different scenarios and the distribution of life consumption under different states. Based on the density distribution, multiple typical scenario operating states of multiple typical state classes are determined.

[0027] Based on the pre-built consumption coefficient model, the operating status of multiple typical scenarios is analyzed, the actual life consumption of each typical scenario is compared with the standard life consumption, and the life consumption coefficient of the corresponding typical scenario is calculated. This coefficient represents the relative life consumption rate of the reactor in a specific scenario. For example, if the life consumption of the reactor is twice that of the standard state in a high-load and high-temperature scenario, the life consumption coefficient of the scenario is 2. Multiple typical scenarios are traversed to obtain multiple life consumption coefficients.

[0028] Since the reactor may operate in a variety of scenarios, the life consumption coefficients of multiple scenarios are weighted averaged based on the distribution density of the scenarios to form an overall scenario life consumption coefficient. This weighted coefficient ensures that when calculating the overall life of the reactor, its operating distribution in each scenario is fully taken into account.

[0029] According to the scenario life consumption coefficient, a dynamic life model is initialized to perform dynamic life prediction, wherein the dynamic life model is a life prediction model under a standard operating state.

[0030] A dynamic life model is established, and the initial model parameters are set according to the data of the standard operating status, such as current, load and temperature. These parameters can be determined by historical data or experimental data, reflecting the life consumption law of the reactor under the standard state. The dynamic life model is initialized under the standard operating state as a basic life prediction framework.

[0031] The scenario life consumption coefficient is introduced into the dynamic life model. According to the distribution of the reactor in different scenarios and its life consumption coefficient, the life consumption rate of the model is adjusted in real time. For example, if the current reactor is in a high-load scenario, the model will double the life consumption rate to reflect the actual situation. As the data is continuously updated, the dynamic life model is also continuously adjusted. When new data comes in, the scenario distribution is recalculated and the scenario life consumption coefficient is updated to ensure that the model can reflect the real state of the reactor in real time.

[0032] The remaining life of the reactor is predicted based on the adjusted dynamic life model. This prediction not only depends on the current state of the reactor, but also comprehensively considers its historical state distribution and expected future operating conditions, thereby achieving accurate life prediction.

[0033] According to the dynamic life prediction results, the target reactor maintenance strategy is updated.

[0034] Based on the prediction results of the dynamic life model, the remaining life of the reactor is determined, including the specific value of the life and the prediction of the time point when the reactor enters the critical life stage, for example, the time when 50% or 80% of the life is about to be consumed. Based on the life prediction, the failure risk of the reactor is evaluated. If the remaining life is too short or the reactor is in a high-risk state, such as a sharp reduction in life under high temperature and high load, preventive maintenance measures must be taken immediately.

[0035] Update the maintenance strategy of the target reactor based on the above analysis results. Specifically, make preventive maintenance adjustments. According to the remaining life and failure risk, adjust the maintenance cycle of the reactor to be more frequent or more precise. For example, if a high-risk state is predicted to be approaching, inspection and maintenance can be arranged in advance to avoid failures. Perform scenario-based maintenance and use scenario operation status distribution data to develop more targeted maintenance plans. For example, for reactors that operate frequently in high-temperature environments, the maintenance strategy is to increase the inspection frequency of cooling devices, or strengthen ventilation and cooling measures in the surrounding environment. Perform condition monitoring. If the dynamic life prediction shows that certain parameters, such as changes in current or load, have a greater impact on the life, the real-time monitoring of these parameters can be strengthened, and alarms can be triggered or maintenance can be performed when the parameters exceed the preset range.

[0036] The updated maintenance strategy for the target reactor is applied, and a maintenance report for the target reactor is generated.

[0037] According to the updated maintenance strategy for the target reactor, the target reactor is maintained. During the maintenance process, each operation step is recorded, including the inspection content, problems found, measures taken, and parts replaced. These records will be used as historical data input in the future to provide reference for subsequent model optimization and strategy adjustment. All data in the maintenance process will be summarized to generate a maintenance report for the target reactor. This maintenance report will be used for future dynamic life prediction model optimization and a new round of maintenance strategy formulation. Through continuous updating and feedback, each maintenance operation and dynamic life prediction will form a closed-loop management system, gradually optimize the management strategy of the reactor, and improve the management efficiency and effect of the reactor.

[0038] Furthermore, the operating status data includes operating condition data and operating environment data between the most recent maintenance completion and the current collection node.

[0039] The starting point for collecting operating status data is set to the time when the reactor was last maintained, and the ending point is the current time. This ensures that the collected data can completely cover the operation of the reactor from the last maintenance to the current state. The data within this time period is collected at certain time intervals. The specific interval depends on the working characteristics of the reactor and the data collection requirements, so as to ensure that real-time change trends are captured and provide data support for subsequent life prediction and maintenance strategies.

[0040] Furthermore, according to the operating status data, the scenario operating status distribution of the target reactor is extracted, before that, the method includes: Based on big data, life consumption samples of the target inductor are obtained, and the life consumption samples include multiple sample operating states and multiple associated sample life performances; the standard operating state and standard life performance of the target inductor are obtained, and in combination with feature engineering, multiple state difference features between the multiple sample operating states and the standard operating states are obtained; in combination with feature engineering, multiple sample life consumption coefficients of the multiple sample life performances and the standard life performances are calculated, wherein the sample life consumption coefficient is the ratio of the sample life performance to the standard life performance; the multiple state difference features are associated with the multiple sample life consumption coefficients, and a numerically driven consumption coefficient model is constructed and trained.

[0041] The big data platform is used to collect the operating data of the target inductor under different working environments and different load conditions, and multiple sample operating states are obtained. These sample operating states cover the operating conditions of different inductors under various conditions, such as "low load, normal temperature", "high load, high temperature", etc. The life performance is associated with each sample operating state. The life performance can be obtained through the actually measured life data (such as the number of operating hours before failure). For example, depending on the operating conditions, the life performance is quantified as a percentage (such as the percentage of remaining life) or a life consumption rate (such as the percentage of consumption rate). The multiple sample operating states obtained and the multiple sample life performances associated therewith are integrated to obtain the life consumption sample.

[0042] Set the standard operating state of the target reactor, that is, the operating parameters of the reactor under ideal or standard conditions. For example, the standard operating state can be defined as "operating at room temperature (25°C), load below the rated value, and humidity within the normal range". Under the standard operating state, the life consumption of the reactor should be minimized. The standard life can be defined as the maximum life value of the reactor through experimental data or based on industry standards. For example, the life of the reactor under standard conditions is 20,000 hours.

[0043] Combined with feature engineering, multiple state difference features from the standard operating state are extracted from multiple sample operating states. These features include temperature difference, load difference, humidity difference, current and voltage difference. The extracted features are constructed as a feature vector in a multidimensional space. The operating state of each sample can be represented by a feature vector. Each dimension in the feature vector represents a deviation from the standard state, such as temperature deviation, load deviation, etc.

[0044] The sample life consumption coefficient is the ratio of the sample life performance of the reactor under a specific state to the standard life performance, which is used to quantify the degree of life consumption under this state. For example, if the standard life performance of a sample is 20,000 hours, and the life consumption of the sample under high temperature and high load conditions is twice that of the standard state, then the life consumption coefficient of the sample in this scenario is 2. The life consumption coefficients are calculated for all samples in batches, and the actual life performance of each sample is calculated by ratio with the standard life performance to obtain the corresponding consumption coefficients, which are used to construct the consumption coefficient model.

[0045] The sample state difference features are associated with their corresponding sample life consumption coefficients. Each sample has a set of feature vectors and a consumption coefficient. These data together constitute a training set. Decision trees, neural networks and other models are selected for modeling. The model is trained using the training set. The model will learn the relationship between the features and the life consumption coefficient. During the training process, the error between the predicted value and the actual value is minimized through optimization algorithms, such as the gradient descent method. A new validation set is used to evaluate the generalization ability of the model through cross-validation methods. According to the validation results, the model parameters are adjusted to improve the prediction accuracy of the model, and finally a consumption coefficient model that meets the preset accuracy is obtained. The model can predict the life consumption coefficient of the reactor according to its real-time operating status, providing a basis for the subsequent accurate prediction of the reactor life.

[0046] Furthermore, extracting the scenario operating state distribution of the target reactor according to the operating state data includes: The operating status data is parsed to perform operating status clustering and generate a status clustering result, wherein the status clustering result includes a plurality of typical status classes; a plurality of typical scenario operating states of the plurality of typical status classes are determined based on density distribution; a plurality of typical scenario operating states are mapped to the consumption coefficient model to obtain a plurality of life consumption coefficients corresponding to the plurality of typical scenario operating states, wherein the life consumption coefficient is a relative life consumption rate under the typical scenario operating state.

[0047] Before clustering, the operating status data is standardized. Since different parameters, such as temperature, current, and load, have different dimensions and magnitudes, standardization can ensure that all features are compared at the same scale. A clustering algorithm, such as K-means clustering, is selected. Through iterative optimization, the data points are assigned to K cluster centers. The K value can usually be determined by the elbow rule, that is, finding the inflection point that makes the error drop most significantly. The standardized operating status data of the reactor is input into the clustering algorithm as a feature. Each data point represents the operating status of the reactor at a certain point in time or within a time period. The clustering algorithm is executed to output multiple typical status classes. Each status class represents the operating mode of the reactor under a specific condition. For example, typical status classes such as "low load and low temperature", "high load and high temperature", and "medium load and high humidity" can be formed. These typical status classes reflect the typical behavior patterns of the reactor under different operating conditions.

[0048] For each typical state class, its density distribution in the entire data set is calculated. The distribution of data points in the feature space can be calculated using the kernel density estimation method to identify the density peak of each state class. State classes with high density represent more common states of the reactor in actual operation, while state classes with low density may be special or abnormal operating states.

[0049] Based on the calculated density distribution, the state class characteristics corresponding to the high-density area are defined as the typical parameter range of each typical class, that is, the typical scenario operating state. For example, a typical scenario corresponds to the operating conditions of "ambient temperature of 30-35°C, load of 70-80%, and humidity of 40-50%". Based on the analysis results, multiple typical scenario operating states are obtained.

[0050] For each typical scenario operating state, the corresponding characteristic parameters, such as temperature, humidity, load, current, etc., are extracted to form a multi-dimensional feature vector. This feature vector fully describes the operating conditions of the scenario. The above feature vector is input into the trained consumption coefficient model. The consumption coefficient model is a numerically driven model that can predict the corresponding life consumption coefficient based on the input feature vector. The model calculates the input feature vector and outputs the life consumption coefficient of the typical scenario. The consumption coefficient represents the relative life consumption rate of the reactor in this specific scenario. The value is the ratio relative to the standard operating state. For example, if the life consumption rate in a typical scenario is twice that of the standard state, the consumption coefficient is 2. Calculate all the multiple typical scenario operating states to obtain the corresponding life consumption coefficient for each scenario. These coefficients are used to quantify the life consumption of the reactor in different typical scenarios.

[0051] Furthermore, extracting the scenario operating state distribution of the target reactor according to the operating state data further includes: Analyze the state clustering results, and count the number of sample points within the multiple typical state classes; calculate the sample proportions of the multiple typical scene operation states based on the multiple number of sample points within the classes, and output them as the scene operation state distribution.

[0052] In the previous clustering step, each running state sample is assigned to a specific state class. Each sample has a cluster label, indicating which typical state class it belongs to. According to the output of the clustering algorithm, all typical state classes are identified. If K-means clustering is used, there are K different state classes. All sample points are traversed and statistics are performed based on the cluster label of each sample point. The number of sample points contained in each typical state class is counted to form a state class-sample point correspondence table. For example, state class 1 has 100 sample points, state class 2 has 250 sample points, and so on.

[0053] First, calculate the total number of sample points in the entire data set, that is, the sum of the number of sample points in all state classes. For each typical state class, calculate the ratio of the number of sample points in the class to the total number of sample points to obtain the sample proportion. Put the sample proportions of all typical state classes together to form a scenario operation state distribution. These data reflect the operating frequency of the reactor in different typical scenarios. According to the sample proportion, identify the state classes with the highest proportion. These states represent the main operating scenarios of the reactor. In the subsequent life prediction and maintenance decisions, these main scenarios can be used as key references.

[0054] Furthermore, the scenario life consumption coefficient is defined according to the scenario operation state distribution, including: Based on the scene operation state distribution, the weighted weights of the multiple life consumption coefficients are determined; the multiple life consumption coefficients corresponding to the multiple typical scene operation states are weighted to obtain a weighted life consumption coefficient, which is defined as the scene life consumption coefficient.

[0055] Based on the distribution of scenario operating states, the sample proportion of each typical scenario is used as the weighted weight of the corresponding life consumption coefficient of the scenario. This weight directly reflects the exposure ratio of the reactor in different operating scenarios. The more common the scenario, the greater the weight.

[0056] For each typical scenario, the calculated life consumption coefficient is referenced. These coefficients quantify the life consumption rate of the reactor in a specific scenario. The life consumption coefficient of each typical scenario is multiplied by its weight, and then these weighted values ​​are summed to obtain an overall weighted life consumption coefficient. This weighted life consumption coefficient represents the average life consumption rate of the reactor in all operating scenarios, taking into account the frequency of occurrence of different scenarios and their impact on life. The calculated weighted life consumption coefficient is used as the scenario life consumption coefficient. It represents the average life consumption performance of the reactor after combining various typical scenarios in actual operation. If the weighted life consumption coefficient is high, it means that the current operating mode of the reactor is consuming faster overall, and it needs to be adjusted in time or the maintenance frequency needs to be increased.

[0057] Furthermore, the method further comprises: Calling the historical maintenance record of the target reactor, wherein the historical maintenance record includes historical operating condition data and fault marks; statistically analyzing the historical maintenance record to obtain the fault probability characteristics of the historical operating condition data, and establishing a mapping relationship between the operating condition data and the fault probability, and storing it as a scenario-based failure rate model of the target reactor; based on the scenario-based failure rate model, evaluating the real-time failure probability of the target reactor, and generating a burst maintenance instruction if the real-time failure probability does not meet the preset failure probability constraint.

[0058] Extract historical operating data related to the operation of the reactor from the reactor database or historical records. These data include parameters such as temperature, current, load, voltage, humidity, etc. These parameters can reflect the operating status of the reactor at different time points and conditions; at the same time, extract the fault records and marks corresponding to these historical operating data. These marks include information such as fault type, occurrence time, and impact range to help identify which operating conditions are more likely to cause faults. Match the fault marks with the operating data of the corresponding time period to form a one-to-one corresponding record as a historical maintenance record. These matched data records are used for subsequent statistical analysis.

[0059] The historical operating condition data is classified according to characteristics such as temperature, current, and load, and then the number of failures and the total number of samples under each operating condition are counted, and the failure probability under each operating condition is calculated. Through multivariate analysis methods, such as multivariate regression and decision trees, the relationship between each operating condition characteristic and the failure probability can be explored. For example, the change in the failure probability when the current exceeds a certain threshold can be analyzed. Based on the analysis results, a mapping model is established. The model associates different operating condition characteristics with the failure probability, thereby predicting the failure probability under a given operating condition. The operating condition characteristics are grouped according to different operating scenarios to form a scenario-based failure rate model. This model can predict the corresponding failure probability based on the characteristics of each scenario.

[0060] The real-time operating condition data of the target reactor is input into the scenario-based failure rate model. The model outputs the real-time failure probability based on the current input data characteristics. This value reflects the possibility of failure of the reactor under the current operating conditions. The calculated real-time failure probability is compared with the preset failure probability threshold. If the failure probability exceeds the set threshold, for example, higher than 20%, it means that the reactor has a high probability of failure. In this case, an emergency maintenance instruction is automatically generated. This instruction may include requiring maintenance personnel to conduct inspections, arrange emergency shutdowns, dispatch spare parts, or take other preventive measures. This process ensures that the failure risks in the operation of the reactor are monitored in real time and responded to in a timely manner, thereby improving the operating safety and reliability of the reactor.

[0061] In summary, the dynamic life reactor maintenance method provided by the embodiment of the present application has the following technical effects: By acquiring the operating condition data and operating environment data of the reactor, the reactor status can be fully monitored. These data provide comprehensive basic data support for life prediction and maintenance strategies. According to the operating data of the reactor, the scenario operating status distribution is extracted, and the scenario life consumption coefficient is defined. It can accurately identify and analyze the life consumption law of the reactor in different environments and working conditions. Through the definition and calculation of the scenario life consumption coefficient, the relative life consumption rate of the reactor in a specific scenario is quantified, which makes the life prediction more accurate and provides a quantitative basis for optimizing the maintenance strategy. According to the scenario life consumption coefficient, the dynamic life model is initialized. The dynamic life model performs dynamic life prediction based on the standard operating state, which greatly improves the accuracy of the reactor life prediction. According to the dynamic life prediction results, the maintenance of the reactor is optimized. The strategy is updated in real time. The maintenance strategy is no longer fixed, but is dynamically adjusted according to the actual operating status, life consumption and risk assessment results to ensure the efficiency and accuracy of maintenance work. By predicting the life status of the reactor in advance, potential faults can be identified and prevented in advance, avoiding sudden failures of the reactor at critical moments. This proactive maintenance method significantly reduces the failure rate and unplanned downtime; the updated maintenance strategy is applied, and maintenance reports are automatically recorded and generated to provide managers with a clear reactor status and management basis. At the same time, automated report generation reduces the errors and uncertainties of manual records, greatly improving management efficiency. Through continuous updating and feedback, each maintenance operation and dynamic life prediction form a closed-loop management system, gradually optimizing the management strategy of the reactor and improving the management efficiency and effectiveness of the reactor.

[0062] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic life reactor maintenance platform, characterized in that: The platform includes: A data acquisition module, the data acquisition module is used to obtain the operating status data of the target reactor, wherein the operating status data includes an operating condition data column and an operating environment data column; A state analysis module, the state analysis module is used to extract the scenario operation state distribution of the target reactor according to the operation state data, and define the scenario life consumption coefficient according to the scenario operation state distribution; A dynamic life prediction module, the dynamic life prediction module is used to initialize a dynamic life model to perform dynamic life prediction according to the scenario life consumption coefficient, wherein the dynamic life model is a life prediction model under a standard operating state; A maintenance strategy generation module, wherein the maintenance strategy generation module is used to update the maintenance strategy of the target reactor according to the dynamic life prediction result; A maintenance report generation module, wherein the maintenance report generation module is used to apply the updated maintenance strategy of the target reactor and generate a maintenance report for the target reactor.

2. A dynamic life reactor maintenance platform as claimed in claim 1, characterized in that: The operating status data includes operating condition data and operating environment data between the most recent maintenance completion and the current collection node.

3. A dynamic life reactor maintenance platform as claimed in claim 2, characterized in that: According to the operating status data, extracting the scenario operating status distribution of the target reactor, before that, the execution steps include: Acquire a life consumption sample of the target reactor based on the big data, wherein the life consumption sample includes a plurality of sample operating states and a plurality of associated sample life performances; Obtaining the standard operating state and standard life performance of the target reactor, and combining feature engineering to obtain multiple state difference features between the sample operating states and the standard operating states; In combination with feature engineering, a plurality of sample life consumption coefficients of the sample life performance and the standard life performance are calculated, wherein the sample life consumption coefficient is a ratio of the sample life performance to the standard life performance; A plurality of the state difference features are associated with a plurality of the sample life consumption coefficients to construct and train a numerically driven consumption coefficient model.

4. A dynamic life reactor maintenance platform as claimed in claim 3, characterized in that: According to the operating status data, extracting the scenario operating status distribution of the target reactor, the execution steps include: Parsing the running status data to perform running status clustering and generate a status clustering result, wherein the status clustering result includes a plurality of typical status classes; Determine multiple typical scenario operating states of multiple typical state classes based on density distribution; Map multiple typical scenario operating states to the consumption coefficient model to obtain multiple life consumption coefficients corresponding to the multiple typical scenario operating states, wherein the life consumption coefficient is a relative life consumption rate under the typical scenario operating state.

5. A dynamic life reactor maintenance platform as claimed in claim 4, characterized in that: Extracting the scenario operating state distribution of the target reactor according to the operating state data, the execution step further includes: Analyze the state clustering results, and count the number of sample points within a plurality of the typical state classes; According to the number of sample points within the multiple classes, the sample proportions of the multiple typical scenario running states are calculated, and the output is the scenario running state distribution.

6. A dynamic life reactor maintenance platform as claimed in claim 5, characterized in that: Defining a scenario life consumption coefficient according to the scenario running state distribution, the execution steps include: Determining weighted weights of a plurality of life consumption coefficients based on the scenario operation state distribution; The plurality of life consumption coefficients corresponding to the plurality of typical scenario operating states are weighted to obtain a weighted life consumption coefficient, which is defined as the scenario life consumption coefficient.

7. A dynamic life reactor maintenance platform as claimed in claim 1, characterized in that: The execution steps of the platform also include: Calling the historical maintenance record of the target reactor, wherein the historical maintenance record includes historical operating condition data and fault marks; Statistically analyzing the historical maintenance records, obtaining the failure probability characteristics of the historical operating condition data, and establishing a mapping relationship between the operating condition data and the failure probability, and storing the results as a scenario-based failure rate model for the target reactor; Based on the scenario-based failure rate model, the real-time failure probability of the target reactor is evaluated, and if the real-time failure probability does not meet the preset failure probability constraint, a sudden maintenance instruction is generated.

8. A dynamic life reactor maintenance method, characterized in that: Based on the implementation of a dynamic life reactor maintenance platform according to any one of claims 1 to 7, the method comprises: Acquire operating status data of the target reactor, wherein the operating status data includes an operating condition data column and an operating environment data column; Extracting a scenario operating state distribution of a target reactor according to the operating state data, and defining a scenario life consumption coefficient according to the scenario operating state distribution; Initializing a dynamic life model to perform dynamic life prediction according to the scenario life consumption coefficient, wherein the dynamic life model is a life prediction model under a standard operating state; Update the target reactor maintenance strategy based on the dynamic life prediction results; The updated maintenance strategy for the target reactor is applied, and a maintenance report for the target reactor is generated.

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